Optimum Bidding of Renewable Energy System Owners in Electricity Markets

Dai, Tao

doi:10.1016/b978-0-08-101041-9.00004-1

Cited by 2 publications

(1 citation statement)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the solutions in both Stage II and Stage I, we will first consider a general distribution of type's demand, and then focus on a discrete distribution of type's demand. The discrete distribution is much more common in the decisionmaking of electricity planning based on the realistic data of load and renewable energy [22]. The discrete distribution can also make the computation tractable, as we will show that the utility only needs to search a set of threshold points, the size of which is linear in the number of demand outcomes and types.…”

Section: Solution Methods For Utility's Pricing Problemmentioning

confidence: 99%

Time-of-use Pricing for Energy Storage Investment

Zhao¹,

Wang²,

Huang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Time-of-use (ToU) pricing is widely used by the electricity utility to shave peak load. Such a pricing scheme provides users with incentives to invest in behind-the-meter energy storage and to shift peak load towards low-price intervals. However, without considering the implication on energy storage investment, an improperly designed ToU pricing scheme may lead to significant welfare loss, especially when users over-invest the storage, which leads to new energy consumption peaks. In this paper, we will study how to design a social-optimum ToU pricing scheme by explicitly considering its impact on storage investment. We model the interactions between the utility and users as a two-stage optimization problem. To resolve the challenge of asymmetric information due to users' private storage cost, we propose a ToU pricing scheme based on different storage types and the aggregate demand per type. Each user does not need to reveal his private cost information. We can further compute the optimal ToU pricing with only a linear complexity. Simulations based on real-world data show that the suboptimality gap of our proposed ToU pricing, compared with the social optimum achieved under complete information, is less than 5%.

show abstract

Section: Solution Methods For Utility's Pricing Problemmentioning

confidence: 99%